The most common way to diagnose cardiac dysfunctions is the ECG signal analysis, usually starting with the assessment of the QRS complex as the most significant wave in the electrocardiogram. Many methods for automatic heartbeats classification have been applied and reported in the literature but the use of different ECG features and the training and testing on different datasets, makes their direct comparison questionable. This paper presents a comparative study of the learning capacity and the classification abilities of four classification methods - Kth nearest neighbour rule, neural networks, discriminant analysis and fuzzy logic. They were applied on 26 morphological parameters, which include information of amplitude, area, interval durations and the QRS vector in a VCG plane and were tested for five types of ventricular complexes - normal heart beats, premature ventricular contractions, left and right bundled branch blocks, and paced beats.